AI that does useful work, not demos
Practical AI for businesses: document search, support assistants, data extraction and automated workflows built on your own data.
Hours lost to reading and sorting
Teams spend a lot of time sorting tickets, summarising documents, pulling details out of invoices and answering the same questions again and again. Regular software can't handle unstructured text like this. AI models can, if they're set up properly.
AI connected to your data
We build AI features around your own documents and systems. That might be a search tool that answers questions from your internal docs (RAG), an assistant that handles common support queries, or a pipeline that pulls structured data out of PDFs and emails.
What's included
- Search your documents
- Ask questions in plain English and get answers from your PDFs, policies and files, with sources.
- Workflow agents
- AI steps that read emails, sort requests and update your systems, with a person checking where it matters.
- Support assistants
- Chat assistants that answer common customer questions and hand the rest to your team.
- Document extraction
- Pull fields out of invoices, contracts and forms into structured data.
- Works with OpenAI, Anthropic, Gemini or open-source models like Llama
- Vector search using pgvector or Pinecone
- Guardrails and data filtering before anything reaches the model
- Answers include links back to the source documents
Usually built with: Python, FastAPI, LangChain, OpenAI API, Pinecone, pgvector, TypeScript
How it runs
- 01
Is AI the right fit?
We look at your data and the task. Sometimes plain automation is the better answer.
- 02
Prepare the data
Load, split and index your documents so the model can find the right parts.
- 03
Build
Prompts, agent logic and a secure API your apps can call.
- 04
Test and monitor
Check answer quality against real examples and keep an eye on accuracy and cost.
Where this helps
Ticket sorting
Incoming support requests are tagged and routed to the right team automatically.
Policy and contract search
HR and legal teams find answers in their documents without digging through folders.
Questions
What happens to our company data?
We use API plans where providers don't train on your data and don't keep it, or we run open-source models inside your own cloud account on AWS or GCP. Your data stays under your control.
What is RAG?
Retrieval-Augmented Generation. Before the AI answers, it searches your documents for relevant passages and uses them as its source. This makes answers much more accurate and lets you check where they came from. It doesn't make mistakes impossible, so we test it against real questions.
Related services
Have a project in mind?
Tell us what you're planning. We'll ask a few questions, work out what you actually need and come back with an honest estimate.